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Articleshttps://doi.org/10.1038/s41593-017-0007-y

1Center for Neural Engineering, Pennsylvania State University, University Park, PA, USA. 2Department of Engineering Science and Mechanics, Pennsylvania State University, University Park, PA, USA. 3Graduate Program in Bioengineering, Pennsylvania State University, University Park, PA, USA. 4Department of Neurosurgery and Department of Biomedical Engineering, Pennsylvania State University, University Park, PA, USA. *e-mail: [email protected]

Spontaneous hemodynamic signals are extensively used in rest-ing-state functional MRI (fMRI) studies to infer neural activity not driven by tasks or stimuli1,2. A cornerstone assumption of these studies is that spontaneous hemodynamic signals are coupled to neural activity in the same manner as hemodynamic signals elicited by sensory stimuli. Recent studies have cast doubt on the one-to-one coupling of neural activity to hemodynamic signals during sensory stimulation3–5, making it critical to determine how spontaneous hemo-dynamic signals are coupled to neural activity. To determine what aspects of neural activity are reported by spontaneous hemodynamic signals during different states, we simultaneously measured neural activity5 and CBV using intrinsic optical imaging4–6 in the vibrissal cortex of awake, head-fixed mice7,8 (Fig. 1a,c and Supplementary Fig. 1a) while monitoring whisker and body movements. Increases in CBV, which show up as decreases in reflectance in intrinsic optical imaging signals, are caused by arterial and capillary dilations7,9,10 and lead to increases in oxygenation, which can be detected with blood oxygen level–dependent (BOLD) fMRI11,12. Although our optical sig-nals originated from the superficial layers, CBV changes have similar dynamics and amplitudes throughout the depth of cortex13, and CBV increases are tightly related to BOLD fMRI signals14.

ResultsBehavior drives spontaneous hemodynamic fluctuationsSince humans and animals continuously engage in small bodily motions15,16 and actively sense their environment17, we monitored body movement and whisker position to detect periods of active behavior and rest (Fig. 1f and Supplementary Fig. 1b). We obtained an average of 254 ± 54 min of simultaneous CBV, neural, and behav-ioral data from each mouse (n = 12 mice; Fig. 1b). We categorized the data into several states: stimulation of the whiskers (contralat-eral to the window) with brief, gentle puffs of air directed towards the whiskers but not the face7; volitional whisking; and rest (Fig. 1b,f and Online Methods). Periods of time greater than 10 s in duration without stimulation in which the animals did not volitionally whisk were defined as true rest. Periods of rest lasting less than 10 s were considered transitions between behaviors and were omitted from subsequent analyses. Extended ( > 2 s) volitional whisker movements

were analyzed separately from brief volitional movements since they were generally associated with additional body motion and distinct vascular responses13. We also used auditory stimulation (air puffs aimed away from the body) and stimulation of ipsilateral whiskers, but as these stimuli primarily elicited volitional whisking behavior (Supplementary Fig. 1h), we focus below on rest, volitional whisking, and stimulation of the contralateral whiskers in subsequent analyses.

We first asked whether there were any differences in the ampli-tude of hemodynamic fluctuations across these different states, as nonstationarities in variance have been postulated to account for most of resting-state connectivity18, though this nonstation-arity has been argued to be spurious and driven by head move-ment and/or sampling variability19. As our signals were unaffected by head motion and as we can parse our data into identified behavioral epochs, we can directly address this issue in our sys-tem. The variance of hemodynamic signal fluctuations in our mice was significantly and substantially larger during all of these states than at rest (Fig. 1e). These result show that, in the awake brain, active sensation and volitional movements drive substantial changes in hemodynamic signals, and variability in the amount of these behaviors contributes to the large trial-to-trial variability observed in hemodynamic signals ‘at rest’20. While our observa-tion that the variance in the CBV signal differed among behavioral states does not rule out statistical or head-motion-related artifacts in generating or contaminating dynamic connectivity patterns seen in human fMRI19, it shows that the amplitude of spontaneous hemodynamic signals will be affected by behavioral state.

Correlations between spontaneous neural activity and CBV changesWe then examined what aspect of neural activity was correlated with the observed CBV changes. Electrophysiology is considerably more sensitive to low levels of neural activity than calcium imaging, which fails to detect a substantial proportion of the action poten-tials, particularly at low firing rates21, and which cannot detect local field potential (LFP) oscillations. Consistent with previous mea-sures showing a relatively tight spatial relationship between CBV changes and neural activity6, we observed that the measured reflec-tance from pixels near the stereotrode recording site was correlated

Weak correlations between hemodynamic signals and ongoing neural activity during the resting stateAaron T. Winder1,2, Christina Echagarruga 1,3, Qingguang Zhang 1,2 and Patrick J. Drew 1,2,3,4*

Spontaneous fluctuations in hemodynamic signals in the absence of a task or overt stimulation are used to infer neural activity. We tested this coupling by simultaneously measuring neural activity and changes in cerebral blood volume (CBV) in the somatosensory cortex of awake, head-fixed mice during periods of true rest and during whisker stimulation and volitional whisking. We found that neurovascular coupling was similar across states and that large, spontaneous CBV changes in the absence of sensory input were driven by volitional whisker and body movements. Hemodynamic signals during periods of rest were weakly correlated with neural activity. Spontaneous fluctuations in CBV and vessel diameter persisted when local neural spiking and glutamatergic input were blocked, as well as during blockade of noradrenergic receptors, suggesting a non-neuronal origin for spontaneous CBV fluctuations. Spontaneous hemodynamic signals reflect a combination of behavior, local neural activity, and putatively non-neural processes.

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https://doi.org/10.1038/s41593-017-0007-ymailto:[email protected]://orcid.org/0000-0003-0208-6795http://orcid.org/0000-0003-4500-813Xhttp://orcid.org/0000-0002-7483-7378http://www.nature.com/natureneuroscience

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to the multiunit average (MUA, a measure of local spiking) during periods of rest in all animals (Fig. 1d). Passive sensory stimulation and volitional whisking both evoked increases in gamma-band power (40–100 Hz) of the LFP and in low-frequency power (< 5 Hz) of the LFP, MUA, and CBV (Fig. 2a,b), similarly to responses in the visual system of primates4,22; however, the MUA response to whis-ker stimulation and volitional whisking varied among animals23. The heart-rate increase induced by the sensory stimulus was small (2.2 ± 1.3%; Supplementary Fig. 2c,e), and the CBV responses were localized to the histologically reconstructed vibrissal cortex and the adjacent multisensory area24 (Supplementary Fig. 1i). The sen-sory-evoked CBV increase was not altered in mice with facial nerve transections, which prevented whisking (Supplementary Fig. 3a), showing that stimulus-elicited whisking had a negligible effect on the sensory-evoked CBV response25. During periods of rest, power increases in the gamma band20 and the MUA were consistently cor-related with CBV increases (Fig. 2c and Supplementary Fig. 4). The correlations between neural activity and CBV in the absence of

sensory stimulation were not substantially affected by facial motor nerve transection (Supplementary Fig. 3b,c), demonstrating that these spontaneous fluctuations in neural activity were centrally generated and were not reafferent signals.

Neurovascular coupling was similar across behavioral statesWe first tested whether neurovascular coupling was similar across different states (sensory stimulation, volitional whisking, and rest). To quantitatively compare the relationship between neural activity and CBV, we calculated the hemodynamic response function (HRF)4,9,26, which is the kernel that relates neural activity to subsequent changes in blood volume. We selected a region of interest (ROI) around the elec-trode (mean area ± s.d.: 0.6 ± 0.1 mm2) based on the resting CBV–MUA correlation map and location of vibrissal cortex (Fig. 1d and Online Methods) to maximize the likelihood of obtaining a strong correla-tion between neural activity and CBV during rest. We compared HRFs calculated from neural and hemodynamic data, segregated by behav-ioral state, to determine whether behavior modulated the relationship

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Fig. 1 | Behavior drives CBV fluctuations. a, Schematic of the experimental setup. b, Total behavioral times, in minutes, for each animal (circles, n = 12 mice). Inset: the median portion of recorded data corresponding to each behavior. c, Schematic of the reinforced thinned-skull windows and implanted stereotrode. d, Representative example from a single animal showing that correlations between CBV (changes in reflectance, –∆ R/R) and neural activity were largest near the stereotrode for all animals (n = 12 mice). Top left: image of a thinned skull window. Filled circle indicates the reconstructed location of the stereotrode tip. Blue shaded regions show the location of whisker barrels. Dashed circular regions designate the locations of ROIs. FL, forelimb; DZ, dysgranular zone. Scale bar, 1 mm. Top right: the pixel-wise correlation coefficients between multiunit power (300–3,000 Hz) and ∆ R/R during rest for the representative animal. Colors denotes the median correlation coefficient for MUA lag times of 0.5–1.5 s (dashed vertical lines, below). Bottom left: cross-correlations between multiunit power and each of the anatomically determined ROIs. Positive lags indicate that CBV follows neural activity. Dashed lines indicate the period over which the median correlation coefficient was calculated in the pixel-wise map. e, The variance in CBV for each state, normalized by the CBV variance during rest. Each circle represents data from single mouse (n = 12). All behaviors show CBV variances significantly larger than resting variance. (Wilcoxon signed-rank test, Bonferroni corrected, extended movement: P = 0.01, z = 3.06; contralateral stimulation: P = 0.01, z = 3.06; ipsilateral stimulation: P = 0.02, z = 2.98; behavioral transitions: P = 0.02, z = 2.98; auditory stimulation: P = 0.05, z = 2.67; volitional whisking: P = 0.02, z = 2.90). Contra, contralateral; ipsi, ipsilateral. f, Example of data from a single trial. Top: periods of volitional whisking (orange), sensory stimulation (dark blue), and rest (light blue). Vibrissa stimulation is denoted by the dark blue triangle and volitional whisks or body movements as orange or gray tick marks, respectively. Second from top: normalized CBV changes (Δ R/R) were measured by averaging the reflectance within the region of interest in the vibrissal cortex. Second from bottom: normalized neural power (Δ P/P) was calculated between 5 and 150 Hz. Bottom: MUA power was summed between 300 and 3,000 Hz.

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between neural activity and subsequent changes in CBV. Because both sensory-evoked and spontaneous increases in CBV were most strongly and consistently correlated with gamma-band and MUA power during rest20 (Fig. 2c and Supplementary Fig. 4), we focused our analysis on these neural measures. Across all states, the HRFs showed that increases in gamma-band power and MUA drove a decrease in reflectance (increases in CBV) with similar latency (Fig. 2d,e and Supplementary Fig. 5a,b), indicating that the temporal dynam-ics of the hemodynamic response was not different across behav-ioral states. The similarity of overall dynamics (time to peak, full width at half maximum) for all conditions implies that neurovascular coupling was constant across behaviors. The shapes of the HRFs were similar regardless of electrode depth (Supplementary Figs. 1g and 5c,d).

Predicting the hemodynamic response from neural activity We then asked how well the CBV signal could be predicted using neural activity for each type of behavior, which bears on the ‘decod-ing’ of fMRI signals27. By neural activity, we mean the electrical activ-ity of neurons (action potentials and the summed action of excitatory and inhibitory postsynaptic potentials (EPSPs and IPSPs, respec-tively) that generate fluctuations in the local field potential) that gen-erate MUA and LFP signals and which, when blocked in a brain region, perturb sensory perception28 or motor behavior29; we do not mean it to refer to the basal metabolic processes that reflect housekeeping functions30 and that persist in the vegetative coma state. Using HRFs that correspond to each type of behavior, we predicted the stimulation-triggered or volitional-whisking-triggered average CBV

response using the stimulation-triggered or volitional-whisking-triggered average neural response, and then calculated the coefficient of determi-nation (R2) between the actual and predicted CBV responses4 (Fig. 3a,c,d, Supplementary Figs. 6 and 7a,b, and Online Methods). Because the neural and CBV signals must be time-locked to an event for averaging, this cannot be done for resting data. We observed a strong agreement between our prediction and the actual average CBV response to vibrissa stimulation using gamma-band power–derived HRFs (median ± inter-quartile range: R2 = 0.77 ± 0.24), which was as strong as previous mea-sures of neurovascular coupling in the visual cortex of primates22,31,32. We also observed a reasonably good agreement between predicted and actual CBV responses to volitional whisking (median ± interquartile range: R2 = 0.21 ± 0.25). These HRFs were then used to predict CBV fluc-tuations on individual stimulation, volitional whisking, and rest trials to determine how much of the ongoing CBV could be explained by neural activity. In comparison to the averaged data, the median R2 of the CBV prediction over individual trials was substantially lower for all behav-ioral conditions when predicting ongoing CBV changes (Fig. 3c,d). A sliding window was used to visualize time-varying differences in pre-diction accuracy on a second-by-second basis during an episode con-taining sensory stimulation, volitional whisking, and rest. This result showed that prediction accuracy was higher during periods containing large CBV fluctuations (such as would be seen during sensory stimula-tion or movement; Figs. 1e and 3b,c,f and Supplementary Fig. 7f,g). CBV changes predicted from gamma-band power during rest were poor pre-dictors of the actual CBV fluctuations (mean ± s.d.: R2 = 0.06 ± 0.03) and predicted significantly less CBV variance than predictions made from

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Fig. 2 | Neurovascular coupling was consistent across behavioral states. a, Average population (n = 12 animals) responses to contralateral whisker stimulation. Top: average normalized stimulus-evoked changes in MUA power. Middle: average normalized stimulus-evoked changes in LFP power as a function of frequency. Bottom: reflectance change (Δ R/R) within the vibrissal ROI. b, Average population (n = 12 animals) responses to voluntary whisker movement. Plots are as in a. Shaded areas indicate the population s.d. c, Mean cross-correlations between neural activity and ∆ R/R calculated during periods of rest show that MUA power (top) and gamma-band power (bottom; 40–100 Hz) were reliably correlated with CBV changes. The shaded regions denote ± 1 s.d. (n = 12 animals). CBV increases lagged neural activity changes by 1.3 ± 0.14 s. d, A comparison of the mean gamma-band derived HRFs. Each HRF was calculated from a single behavioral state—stimulation (sensory-evoked, dark blue), volitional whisking (orange), and rest (light blue)—and averaged across animals (n = 12). See Supplementary Fig. 5 for individual data. e, As in d but for HRFs calculated from MUA.




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Fig. 3 | Predicting CBV changes from neural activity using HRFs. a, An example of the gamma-band power–derived (teal blue) and MUA-derived (gray) HRFs predicting the average stimulus-triggered CBV response (black) for a single animal. Both predictions have the same dynamics as the measured response and capture 95% of the variance (gamma-band R2 = 0.955; MUA R2 = 0.954). b, Predictions of ongoing CBV for a single trial. Top: predictions by gamma-band (teal blue, R2 = 0.29) and MUA-derived (gray, R2 = 0.20) HRFs. Bottom: goodness-of-fit (R2) for 8-s sliding windows. Colored triangles indicate sensory stimuli. Orange tick marks indicate volitional whisking events. c, Population summary of the goodness of fit (R2) of gamma-band power–derived CBV predictions to measured CBV, separated by behavior. Circles represent the median R2 for each animal (n = 12). R2 values calculated on predictions of the average behavior-triggered CBV are outlined in black (n = 12, two-way ANOVA, P = 8 × 10−6, F2,11 = 21.02; post hoc: paired t test, Bonferroni corrected, sensory evoked vs. extended movement: P = 1 × 10−3, t11 = 4.36; sensory evoked vs. volitional whisking: P = 6 × 10−6, t11 = 8.02; extended movement vs. volitional whisking: P = 0.14, t11 = 1.57). Colored circles without black outlines indicate the median R2 values calculated for predictions of CBV during individual periods of behavior (Friedman test, P = 3 × 10−6, χ 23,33 = 26.3; post hoc: Wilcoxon signed-rank test, Bonferroni corrected, sensory evoked vs. extended movement: P = 0.11, Z = 2.35; sensory evoked vs. volitional whisking: P = 0.02, z = 2.98; sensory evoked vs. rest: P = 0.01, z = 3.06; extended movement vs. volitional whisking: P = 0.01, z = 3.05; volitional whisking vs. rest: P = 0.51, z = 1.73; extended movement vs. rest: P = 0.02, z = 2.98). Filled black circles show population medians. d, As in c but for MUA-derived HRFs (averaged CBV: Friedman test, P = 0.02, χ 22,22 = 8.17; post hoc: Wilcoxon signed-rank test, Bonferroni corrected, sensory-evoked vs. extended movement: P = 0.045, z = 2.43; sensory evoked vs. volitional whisking: P = 0.02, z = 2.67; extended movement vs. volitional whisking: P = 0.64, z = –0.47; individual CBV: Friedman test, P = 8 × 10−3, χ 23,33 = 11.8; post hoc: Wilcoxon signed-rank test, Bonferroni corrected, sensory evoked vs. extended movement: P = 0.25, z = 2.04; sensory evoked vs. volitional whisk: P = 0.03, z = 2.82; sensory evoked vs. rest: P = 0.09, z = 2.43; extended movement vs. volitional whisk: P = 0.94, z = –0.08; extended movement vs. rest: P = 0.88, z = –0.16; volitional whisk vs. rest: P = 0.93, z = 0.08, n = 12). e, The MUA-derived HRF was better at predicting resting CBV fluctuations than the gamma-band power derived HRF (paired t test, P = 0.02, t11 = –2.68). f, An example illustrating that ongoing animal behavior, in this case grooming, can lead to very good prediction of measured CBV. Compare to b; gamma-band-power-derived prediction (teal blue): R2 = 0.48, MUA-derived prediction (gray): R2 = 0.43. Video frames showing periods of animal behavior and rest are displayed below the data. Arrows point to the data corresponding to the video frame.





MUA during rest (mean ± s.d.: R2 = 0.14 ± 0.09; Fig. 3e). The low R2 was not due to the quality of HRFs, since the HRF captured the majority of the averaged stimulus-triggered CBV variance (Fig. 3a,c,d). Lower-frequency bands (0.1–8 Hz and 10–30 Hz) of the LFP were essentially uncorrelated with resting hemodynamic signals (median R2 < 0.02 for both frequency bands; Supplementary Fig. 6). Additionally, when ani-mals were more behaviorally active, the CBV predicted from neural activity was in good agreement with measured CBV changes (Fig. 3f). The disagreement between the predicted and actual CBV on an event-by-event basis was not due to instrumentation noise (Supplementary Fig. 1d,e), and spontaneous CBV fluctuations were not substantially cor-related with fluctuations in heart rate (Supplementary Fig. 2g), ruling out a systemic cardiovascular origin33. Lastly, subtracting off the global hemodynamic signal within the window (Supplementary Fig. 7e) did not substantially improve the quality of the prediction, indicating that the signals were not generated by global blood-volume fluctuations but rather had complicated spatiotemporal dynamics (Supplementary Movie 1). These results show that the hemodynamic response was strongly coupled to the gamma band of the LFP and the MUA when neural activity was high, regardless of the source of the neural activity, but that the correlation broke down when neural activity was low, such as during rest.

Separate, additive components within the hemodynamic signalWhat could account for the much lower correlation between the predicted and actual CBV for individual events versus the average and for the poor performance of the HRF in predicting CBV fluctuations during rest? As our predictions of sensory-evoked CBV changes from neural activ-ity were consistent with previous primate studies4,20,22,32 (see Discussion) and performed well during behaviors with large modulations of neural activity (Fig. 3f), it seems unlikely that the weak correlations between neural activity and CBV changes at rest could be due to our experimen-tal conditions. We hypothesized that there are two processes underly-ing local CBV changes: a neuronal source that drives vasodilation when neurons are active34 and a second component that is uncorrelated to

the measured local neural activity and that additively interacts with the neurally evoked CBV component (Fig. 4a). In this model, the additional CBV component would be removed by averaging over stimulus presen-tations, since the timing of neural events would be random with respect to the phase of these uncorrelated CBV fluctuations. This hypothesis accounts for the substantially better predictions of behavior- averaged CBV changes from neural activity compared to indi-vidual events. In addition, the larger neural responses to sensory stimulation (versus volitional whisking or rest) would increase the amplitude of the neurally evoked hemodynamic signal with respect to the putatively non-neuronal component. The increased amplitude of the neurally evoked CBV would also increase the fraction of the signal that could be predicted from neural activity and account for the differences in HRF performance in predicting CBV changes from neural activity among behaviors (Fig. 3c,d and Supplementary Fig. 7f,g).

Our hypothesis makes two testable predictions. First, the two CBV components should combine additively, so that the residuals (corresponding to the portion of CBV uncorrelated with local neu-ral activity) of the predicted CBV should have constant amplitude across states. To test for additivity, we compared the CBV variance around the mean stimulus-evoked and whisking-evoked responses to the CBV variance at rest. We found that the CBV variance during rest was not different from the trial-to-trial variance during behavior (Fig. 4b). The same pattern held true when we subtracted the neurally evoked CBV component (predicted using either the gamma-band or MUA-derived HRFs) from the measured CBV signal for each of the behavioral conditions and calculated the variance of the residu-als. The level of putatively non-neuronal noise was roughly constant across conditions, as the ratio of the residuals during sensory-evoked and volitional non-neuronal periods to those during rest (as mea-sured by the root-mean-squared error between the predicted and actual CBV signal) was close to 1 (Fig. 4c,d). The additive noise hypothesis also predicts that CBV should be better predicted by neu-ral activity during periods with greater modulation of spontaneous (non-experimenter-evoked) neural activity, which we also saw in our

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Fig. 4 | Variance of residual signal is similar across behavioral states. a, Top: voluntary whisking causes moderate increases in neural activity above background, while vibrissa stimulation causes large increases in neural activity. The HRF is convolved with the neural activity to give the neurally evoked component of the CBV signal. Bottom: schematic illustrating the hypothesis that the measured hemodynamic signal (bottom left, light blue) is comprised of a neurally evoked CBV component (top left, dark blue) and a separate, additive CBV component that is uncorrelated with the measured local neural activity (middle left, gray). The neurally evoked CBV component can be subtracted out using the mean response from a given behavior (top right, middle right). The variance of the residuals after subtracting the neurally driven CBV component will then be equal to the variance of the additive CBV component (bottom right). b, Consistent with the hypothesis in a, the variance in the mean CBV response after subtraction of the mean-evoked response was not significantly different from the variance in the CBV during rest (t test: sensory-evoked: P = 0.86, t11 = –0.18; whisking: P = 0.61, t11 = 0.52). Each circle represents a single animal (n = 12); bar indicates the mean. c, Comparison of the root-mean-squared error (RMSE) of the gamma-band-derived HRF during stimulation and volitional whisking normalized by the RMSE during rest for all animals (n = 12). Bars indicate the means across animals. RMSE was similar between behaviors for the gamma-band (t test, Bonferroni corrected, sensory-evoked vs. rest: P = 0.06, t11 = 2.70; sensory-evoked vs. whisk: 0.73, t11 = 0.35; volitional whisk vs. rest: P = 0.07, t11 = 2.66). d, As in c but for MUA-derived HRF predictions (Wilcoxon signed-rank test, Bonferroni corrected, sensory-evoked vs. rest: P = 0.01, z = 2.90; sensory-evoked vs. whisk: P = 0.18, z = 1.33; whisk vs. rest: P = 0.07, z = 2.27).




Articles Nature NeuroSCieNCedata (Supplementary Fig. 7f–h). These analyses support the hypoth-esis that there are two additively interacting components of the CBV, one of which is linked tightly to the measured neural activity.

Origin of spontaneous hemodynamic fluctuationsThere are several mechanisms that could give rise to the compo-nent of the CBV signal that was not predicted by our measures of neural activity. One possibility is that it could be local or distant groups of neurons whose activity is not detectable in the LFP or MUA but can elicit a vasodilatory response, which we tested below. Examining the power spectra of the predicted and actual CBV changes over the entire trial (Supplementary Fig. 7c), we saw the largest difference in the 0.1–0.3-Hz range, and the power spectrum of the HRF prediction error during periods of rest (Supplementary Fig. 7d) showed a similar peak at 0.2 Hz35. The spectral power of the residual CBV indicates that it is unlikely that the spontaneous activity of astrocytes drove these additive CBV oscillations because the spontaneous calcium signals in cortical astrocytes are nearly 100 times slower than the CBV fluctuations we see here (0.1–0.3 Hz versus 0.005 Hz)36. However, the CBV fluctuations could be due to intrinsic oscillations in the membrane potential (and contractility) of the smooth muscle around arterioles that additively interact with vasodilatory signals from neurons, similarly to the way intrinsic membrane oscillations can sculpt response dynamics in neurons. Vascular oscillations are a plausible mechanism for the persistent CBV changes because isolated arterioles show spontaneous fluc-tuations in their membrane potentials and diameters in the same frequency range that we observed in the residual of our CBV pre-diction37. Vessel diameter is linearly related to smooth muscle mem-brane voltage so long as the vessel is not maximally dilated38, and our stimulus-induced dilations are in this linear regime (9% peak arteriole dilation to punctate contralateral vibrissa stimulation7 versus > 40% maximum arteriole dilation39). In contrast to neurally evoked fluctuations, fluctuations of a vascular origin should persist when neural activity is silenced.

We tested whether silencing local spiking blocks spontaneous CBV fluctuations during rest by infusing muscimol into the cortex via a chronically implanted cannula. The efficacy of the muscimol infusion was monitored with a stereotrode placed 1.5 ± 0.5 mm from the cannula (Fig. 5a and Supplementary Figs. 1g and 8a). A semicir-cular ROI, centered on the cannula and with a radius specified by the distance between the electrode and cannula, was selected to ensure that the ROI only included silenced cortex. Since we observed clear suppression of neural activity with electrodes placed ~2 mm away, the infusion should affect all cortical layers (as the cannula was placed ~200 µ m below the surface and the mouse cortex is ~1.2 mm thick).

Muscimol infusion caused a strong decrease in the sensory-evoked and baseline MUA, as compared to control artificial cerebrospinal fluid (aCSF) infusions (Fig. 5b and Supplementary Figs. 7i, 8a, and 9a). Similarly, the standard deviation in the MUA signal during peri-ods of rest was decreased by 95 ± 2% of the control (Online Methods), indicating a nearly total silencing of spontaneous local spiking (Fig. 5g). The standard deviation of gamma-band power was reduced by 73 ± 15% (Fig. 5j). If resting hemodynamics arose primarily from local spiking, then the spontaneous fluctuations in CBV should show profound reductions in amplitude, essentially disappearing. However, all animals showed a significant and substantial remaining CBV fluctuations during periods of rest, despite the nearly complete elimination of local neural activity (resting CBV root-mean-squared amplitudes after muscimol infusions were 84 ± 41% of aCSF infu-sions, 95% confidence interval: [62–112%], bootstrap of the popula-tion mean, 2,000 resamples; Fig. 5c,d,g,j and Supplementary Fig. 8a). Muscimol infusions did not appreciably change the spatial patterns of the resting CBV fluctuations or the spectral power of the oscillations compared to the HRF prediction residuals (Supplementary Videos 2 and 3 and Supplementary Fig. 7d).

We also measured the diameters of arterioles after local infusion of aCSF or muscimol using two-photon microscopy7,39. We mea-sured from arterioles that would be well within the silenced region (mean vessel distance from infusion cannula: 0.75 ± 0.14 mm). These single-arteriole measures also showed small decreases in normal-ized fluctuation amplitudes at rest after muscimol infusions relative to aCSF infusions (resting root-mean-squared diameter fluctuations after muscimol infusions were 80 ± 25% of aCSF infusions, mixed-effects ANOVA, P = 0.0015, t36 = 3.43, n = 19 pial arterioles in 4 mice; 71 ± 30%, n = 8 penetrating arterioles in 2 mice; P = 0.36, t14 = 0.94; Fig. 5d,e). These results indicate that at least half of the resting CBV fluctuations were not directly linked to local neural spiking.

Several studies have suggested that presynaptic activity alone, in the absence of postsynaptic spiking, is sufficient to drive the hemo-dynamic response12,40,41. Hence, the remaining CBV oscillations could arise from glutamatergic release from distant cortico–corti-cal or subcortical projections, which would not be blocked by the local muscimol infusions. To determine whether presynaptic activity of long-range glutamatergic projections could account for the rest of the CBV oscillations through ionotropic receptors, we infused a cocktail of muscimol and ionotropic glutamate receptor antagonists, 6-cyano-7-nitroquinoxaline-2,3-dione (CNQX) and (2R)-amino-5-phosphonopentanoic acid (AP5), which inhibit AMPA and NMDA receptors, respectively (n = 4 mice; Online Methods). We again observed a strong reduction in the local multiunit activity (96 ± 2%; Fig. 5h), as well as a strong reduction in gamma-band power (87 ± 9%; Fig. 5k and Supplementary Figs. 8b and 9b). However, the reduction in CBV amplitude oscillations at rest (40 ± 20%; Fig. 5h,k) was not significantly lower than with the muscimol-only infusion, and did not account for the CBV or arteriole diameter oscillation (12 ± 47% reduction, n = 18 vessels in 3 mice; Fig. 5f) that remained in the absence of local neural activity. These results indicate that, although presynaptic activity may contribute to some aspect of the spontaneous CBV fluctuations at rest, a large portion of the CBV fluctuations were not driven by either presynaptic neural activity or postsynaptic spiking.

Another possibility is that the remaining resting CBV fluctua-tions were driven by nonglutamatergic modulatory input12,42, partic-ularly noradrenergic input, as it has been shown in human studies43 to play a role in resting state signals. To determine whether adren-ergic input drove the remaining CBV oscillations after silencing local neural activity, we infused a cocktail of muscimol and adren-ergic receptor blockers prazosin, yohimbine, and propranolol. The infusion of this cocktail again suppressed local spiking (94 ± 5%, n = 5; Fig. 5i and Supplementary Figs. 8c and 9c) and gamma-band power (79 ± 15%; Fig. 5l), and it reduced the amplitude of the CBV fluctuations by 40 ± 17%, though not to a level significantly lower than the infusion of muscimol alone (n = 5, unpaired t test, one-sided, P = 0.12, t12 = 1.24). These data indicate that there was mini-mal contribution of adrenergic signaling to the generation of the ongoing CBV fluctuations in the absence of local spiking.

Because the cells in the vasculature are electrically coupled, there is a possibility that dilations in distant areas could propagate into the silenced region, which has been seen before in visual cortex44. To determine whether propagation of vascular dilation could account for the observed CBV changes in the silenced region, we calculated the cross-correlation between CBV fluctuations within the ROI and the rest of the window from data taken after the infusion of musci-mol and aCSF. If propagation of dilation accounts for the remaining CBV fluctuations, the correlation coefficient between these regions should increase and the time lag at which maximum correlation occurs should be delayed following muscimol infusion. We found that neither correlation nor lag times were significantly different between aCSF and muscimol trials (n = 9 mice, correlations: paired t test, P = 0.12, t5 = –1.9; lags: paired t test, P = 0.3, t5 = –1.17). This indicates that the remaining CBV fluctuations did not propagate





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Fig. 5 | spontaneous CBV fluctuations continue in the absence of neural activity and noradrenergic input. a, Top: schematic of the window–electrode–cannula preparation. Bottom: an image of a thinned skull window with cannula and electrode implants. The shaded yellow area indicates the ROI for CBV measurements. Scale bar, 1 mm. b, The average stimulation-evoked MUA following muscimol (n = 62 stimuli) and aCSF (n = 93 stimuli) infusion in a representative animal, showing reduced baseline and sensory-evoked MUA (top) and average stimulation-evoked increase in CBV (bottom) within the ROI in a. c, The average normalized pixel-wise CBV response to contralateral whisker stimulation (0.5–2 s after stimulation) across the thinned skull window for a single animal after aCSF infusion (top left, n = 93 stimuli) and after infusion of muscimol (top center, n = 62 stimuli). Top right shows the difference between aCSF infusion and muscimol infusion in the stimulation-triggered CBV response. The root-mean-squared deviation (σ ) of resting CBV across the thinned skull window after aCSF infusion (bottom left) and after muscimol infusion is shown in the bottom center. The difference in the magnitude of the fluctuations under both conditions is shown in the bottom right. Example shows the same animal depicted in a and b. d, Example of resting CBV fluctuations following aCSF (black) and muscimol (blue) infusions in a single animal (top, same animal as in a). Bottom: normalized diameter measurements made from a single pial arteriole with two-photon laser scanning microscopy following aCSF (black) and muscimol (blue) infusions from a single animal during periods of rest. e, Population summary of the root-mean-square deviation in pial arteriole diameters (circles, n = 19) after muscimol infusion for 4 animals. Orange circle is the arteriole in lower plot in d. Bars indicate the mean for each animal. Vessel diameter oscillations were reduced following infusion of muscimol compared to aCSF (mixed-effects ANOVA, P = 0.0015, t36 = 3.43) f, As in e but for measurements taken after infusion of muscimol + CNQX + AP5 cocktail. We found no significant difference in the amplitudes of the normalized diameter fluctuations between muscimol + CNQX + AP5 and aCSF infusions (mixed-effects ANOVA, n = 18 vessels in 3 animals, P = 0.22, t34 = 1.26). g, Comparison of the root-mean-square deviation of resting MUA and CBV fluctuations. Black circles and error bars outside the axes show the population means and s.d. Orange circle is the mouse in a–c. The dashed gray line is unity. h, As in g but with a muscimol + CNQX + AP5 cocktail infusion. The resting CBV oscillation amplitude was not reduced compared to muscimol-only infusions (n = 4, unpaired t test, one sided, P = 0.15, t11 = 1.11). i, As in g but with an infusion of muscimol + prazosin + yohimbine + propranolol cocktail. The resting CBV oscillation was not significantly reduced as compared to muscimol-only infusions (n = 5, unpaired t test, one-sided, P = 0.12, t12 = 1.24). j, The root-mean-squared deviation of gamma-band neural power compared to CBV during periods of rest following muscimol infusion for all animals. Black circles and error bars outside the axes show the population means and s.d. k, As in j but for infusion of muscimol + CNQX + AP5 cocktail. The resting gamma-band power oscillations were reduced 87% compared to in controls but were not significantly different from reductions due to muscimol-only infusions (unpaired t test, one-sided, P = 0.06, t11 = 1.73). l, As in j but for an infusion of muscimol + prazosin + yohimbine + propranolol cocktail. The oscillations in gamma-band power were reduced 79% compared to in aCSF controls, which was not significantly different from results of muscimol-only infusions (unpaired t test, two sided, P = 0.52, t12 = 0.66).




Articles Nature NeuroSCieNCefrom other regions within the window44 (Supplementary Fig. 10). Taken together, these results indicate that resting CBV consists of contributions from local neural populations but that at least half of the resting CBV amplitude is not due to local neural processing, glutamatergic input, or adrenergic modulation. These remaining CBV oscillations peaked between 0.1 and 0.3 Hz, which is spectrally indis-tinguishable from neurally evoked CBV (Fig. 4a and Supplementary Fig. 7d), and were uncorrelated to sensory stimulation and movement (Fig. 4b–d).

DiscussionComparison to previous measures of spontaneous and sensory-evoked neurovascular couplingThe correlations between neural activity and CBV we observed in the absence of sensory stimulation agreed well with those obtained in awake monkeys (mean ± s.d: r = 0.21 ± 0.07; Supplementary Fig. 4 versus Schölvinck et al.20: 0.2 < r < 0.3). We found a median R2 = 0.77 for our gamma-band-power-derived prediction of stimulus-triggered average CBV, comparable to previous work in anesthetized primates (Logothetis et al.31 R2 = 0.76 and R2 = 0.9 reported for two different experimental sessions). Our trial-by-trial R2 values for contralateral sensory stimulation were 0.34 and 0.30 for the gamma-band and MUA signals, respectively: very similar to those obtained by Goense et al.22 in awake primates. It seems unlikely that we are missing a sig-nal detectable by conventional electrophysiological techniques.

Comparison of our sensory-evoked HRF prediction accuracy to other studies needs to take into account the strength and duration of the stimulus used, as well as whether this is for averaged stimulus-evoked hemodynamic changes or calculated on a trial-by-trial basis. The visual stimulations in these previous studies produced substan-tially larger and more prolonged spiking activity increases than our whisker stimulation (Logothetis et al.31: 10 s.d. above rest, stimulus 4–24 s duration; Goense et al.22: 8–10 s.d. above rest, stimulus 15 s duration; Sirotin & Das4: ~400% above resting rates, stimulus ~5 s duration; this study: 3 s.d. or 170% above rest, stimulus 10 ms dura-tion). Notably, the R2 between the actual and HRF-predicted CBV decreases as the stimulus intensity is reduced32. When we compare our results with stimuli that elicit comparable changes in neural activity with the awake primate data (low-contrast (~12.5%) visual stimulation in Cardoso et al.32: ~200% increase; this paper: 170% increase), we are in good agreement (R2 values: Cardoso et al.32: 0.26, in their Supplementary Fig. 3; this paper: 0.31).

Recent work measuring cerebral blood volume and changes in the fluorescence of the genetically encoded calcium indicator GCaMP in awake head-fixed mice during periods without locomotion, but no other behavioral monitoring, reported good agreement between CBV and fluorescence changes45. However, behaviors such as whisking, grooming (Fig. 3f), and brief (< 1 s) movement events9,46 may contaminate these signals. Lastly, ‘ground truth’ experiments that simultaneously measure spiking activity with electrophysiol-ogy and fluorescence of calcium indicators show that even the best spike-detection algorithms detect far fewer than half the spikes and that this issue is worse at low firing rates, the regime under which resting-state activity is likely to be taking place21.

Implications of potential non-neuronal signals for hemodynamic imagingOur results have several important implications for the interpreta-tion of the neural correlates of hemodynamic signals. We showed that ‘spontaneous’ hemodynamic signals are driven by behaviors, such as active sensing and small body movements, that are typi-cally ignored in human and animal imaging studies. These move-ments drive spatially localized hemodynamic signals and arterial dilations5,39, and, because of the spatial specificity of these hemo-dynamic changes, a global regression procedure will be ineffective in removing these movement-evoked activations and may even

introduce artifacts47. Our results particularly call into question previous studies that used spontaneous hemodynamic signals to assay neural differences that accompany disease, aging, and other conditions, as slight differences in behavior could drive differ-ences in spontaneous dynamics independent of any underlying differences in neural activity.

We demonstrated that unlike the sensory-evoked response, in the true resting state at least 50% of the CBV signal is unrelated to local cortical processing, glutamatergic input, or noradrener-gic modulation. While our results are consistent with the idea that these uncorrelated fluctuations in CBV have a non-neuronal origin, we cannot completely rule out other possible origins. For example, the spontaneous fluctuations could be driven by a subpopulation of vasodilatory neurons48 or the recruitment of local astrocytes34 whose activity we cannot detect in the MUA or LFP. However, to account for the additive CBV signal, any driver must have activity that is completely uncorrelated with other neurons and must have activ-ity patterns that are unaffected by sensory stimulation or behav-ior. Additionally, for this alternative hypothesis to be true, when all of the vasodilatory input to the blood vessels is silenced during the local pharmacological infusions, the arterioles would need to have a homeostatic process that drives similar oscillations in diam-eter when the neural input is removed. To our knowledge, there are no such candidate neurons or processes in arteries. However, we hope that these results will motivate further investigations in to the details of neurovascular coupling and smooth muscle biophys-ics34. If these persistent CBV oscillations are of vascular origin, their function is not clear, though vascular pulsations have been impli-cated in driving fluid exchange in the lymphatic system49.

Whatever the origin of the spontaneous CBV oscillations, our results clearly show that, in the absence of overt stimulation or behavior, hemodynamic signals are very poorly correlated with any conventional measure neural activity (Fig. 3). This poor cor-relation is in direct contrast to sensory-evoked and behaviorally evoked hemodynamic signals, which are relatively tightly coupled to the underlying neural activity. Subtracting off the global signal (Supplementary Fig. 7e) did not substantially improve this corre-lation, suggesting that there is no simple fix. Because of technical considerations, BOLD fMRI signals have even lower correlations with neural activity than the CBV signals we recorded here11, making the problems observed here even more acute for human studies. This ‘noise’ will limit the ability to detect and accurately infer ongoing neural activity and can only be surmounted by massive averaging. However, we note that these results do not refute the existence of functional connectivity, which has been validated using direct assays of neural activity50.

In light of these results, we propose fundamental changes in the way resting-state studies should be performed and interpreted. First, it is essential to measure behavior in a very detailed way dur-ing imaging to ensure that behavioral variability does not drive differences in spontaneous activity between groups or across conditions. Small differences in the frequency of active-sensing behaviors and movements will drive large differences in sponta-neous hemodynamic signals. Second, during periods of true rest, hemodynamic signals should not be interpreted as being solely driven by electrical activity in neurons. Rather, these signals have contributions from other processes that are additive to, and uncor-related with, neurally evoked CBV changes.

Note added in proof: A recent paper74 also found correlations between gamma band power and spontaneous hemodynamic sig-nals in awake mice.

MethodsMethods, including statements of data availability and any asso-ciated accession codes and references, are available at https://doi.org/10.1038/s41593-017-0007-y.



https://doi.org/10.1038/s41593-017-0007-yhttps://doi.org/10.1038/s41593-017-0007-yhttp://www.nature.com/natureneuroscience

ArticlesNature NeuroSCieNCeReceived: 6 April 2017; Accepted: 20 September 2017; Published online: 6 November 2017

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AcknowledgementsWe thank L. Abbott, M. Adams, P. Blinder, D. Feldman, and N. Zhang for comments on the manuscript, and J. Berwick, D. Kleinfeld, and C. Mateo for discussions. This work was supported a Scholar Award from the McKnight Endowment Fund for Neuroscience, and grants R01NS078168, R01EB021703, and R01NS079737 from the NIH to P.J.D.

Author contributionsA.T.W. and P.J.D. designed the experiments, A.T.W., C.E., and Q.Z. performed experiments and analyzed the data, A.T.W. and P.J.D. wrote the paper.

Competing financial interestsThe authors declare no competing financial interests.

Additional informationSupplementary information is available for this paper at https://doi.org/10.1038/s41593-017-0007-y.

Reprints and permissions information is available at www.nature.com/reprints.

Correspondence and requests for materials should be addressed to P.J.D.

Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.



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Articles Nature NeuroSCieNCeMethodsAnimals and procedures. All procedures outlined below were conducted in accordance with guidelines from the Institutional Animal Care and Use Committee (IACUC) of Penn State. Data were acquired from 42 male C57-BJ6 mice (Jackson Laboratory) between 3 and 8 months of age (12 for HRF and behavior measurements, 3 for window-only controls, 3 for transected motor nerve controls, 7 for two-photon laser scanning microscopy (2PLSM), and 17 for cannula implantation and infusion experiments). Mice were given food and water ad libitum and maintained on 12-h light/dark cycles in isolated cages during the period of experiments. All imaging took place during the light cycle period. Sample sizes were chosen to be consistent with previous studies6–9. Data collection and analysis were not performed blind to the conditions of the experiments. Drug infusions were counterbalanced across animals. See the Life Sciences Reporting Summary for more details.

Surgery. Electrode, cannula, and window implantation procedure for intrinsic optical signal (IOS) imaging experiments. Mice were anesthetized with 2% isoflurane (in oxygen) for all surgical procedures. A custom-machined titanium metal bar, used to fix the mice in place during imaging, was attached to the skull with cyanoacrylate glue (Vibra-Tite, 32402). The bar was positioned along the midline and just posterior to the lambda cranial suture. A reinforced thinned-skull window was created over the left hemisphere as described previously5,51. A 4–10-mm2 area of skull was thinned over the whisker representation of somatosensory cortex (Fig. 1c,d). A Teflon-coated tungsten-wire (AM systems, #795500) stereotrode was inserted into the barrel cortex at 30–45° from the horizontal along the rostrocaudal axis using a micromanipulator (Sutter Instruments, MP285) through a small hole made at the edge of the thinned area, so that the electrode tips were within the window (Fig. 1c). The hole was sealed with cyanoacrylate glue. In a subset of mice (n = 17), a small craniotomy was made at the edge of the thinned area of skull, and a cannula (Plastics One, C315DCS, C315GS-4) was inserted into the upper layers of cortex (Fig. 5a and Supplementary Figs. 1g and 8). The cannula was attached to the skull with cyanoacrylate glue and dental acrylic. The thinned area of skull was reinforced with a fitted #1 glass coverslip (Electrode Microscopy Sciences, #72200). Three self-tapping, 3/32-inch #000 (J.I. Morris) screws were inserted into the contralateral parietal and frontal bone and ipsilateral frontal bone. A stainless-steel wire (A-M Systems, #792800) was wrapped around the screw implanted in the frontal bone contralateral to the window and used as an electrical ground for neural recordings. The titanium bar, screws, and tungsten wires were secured with black dental acrylic resin (Lang Dental MFG. Co., REF 1530) to minimize light reflection. The animals were allowed to recover for 2–3 d before habituation. Neither electrode nor cannula implantation affected the hemodynamic response (Supplementary Fig. 1f).

Cannula and window implantation for two-photon laser scanning microscopy (2PLSM). As above, mice were anesthetized with isoflurane for all surgical procedures. A titanium head bar was placed as described above, and a 4–6-mm2 polished and reinforced thinned-skull window was created over the forepaw/hindpaw representation of somatosensory cortex of the left hemisphere51. After polishing the skull with 3F grit (Covington Engineering, Step Three 3F-400), a small craniotomy was made to insert the cannula into the upper layers of cortex near the forepaw/hindpaw representation of cortex. The thinned area of the skull was reinforced with a fitted #0 glass coverslip (Electrode Microscopy Sciences, #72198). Two self-tapping screws were inserted into the contralateral parietal and ipsilateral frontal bone. The titanium bar, cannula, and screws were secured with black dental acrylic resin. The animals were allowed to recover for 2–3 d before habituation.

Facial nerve transections. Animals were anesthetized with 2% isoflurane and bilateral incisions were made posterior to the whisker pad. The facial nerve was transected with iris scissors25, and the incisions were sutured. Topical antibiotic ointment was applied (Neosporin), and animals were allowed to recover for 2–3 d before habituation to head fixation. Animals were monitored to verify that no vibrissa movement occurred following transection.

Histology. At the end of the experiment, animals were deeply anesthetized with 5% isoflurane. The mice were transcardially perfused with heparinized saline and then fixed with 4% paraformaldehyde. Fiduciary marks were made in each of the corners of the cranial window. The brains were extracted and sunk in a 30% sucrose/4% paraformaldehyde solution. The flattened cortexes were sectioned tangentially (60 µ m per section) using a freezing microtome and stained for the presence of cytochrome oxidase (CO). Whisker barrels were visible in layer IV of CO-stained sections, allowing the alignment of the windows and stereotrodes with respect to the barrel cortex52,53. The laminar locations of the stereotrodes were categorized as supragranular, granular, or infragranular by identifying the first cortical section without visible electrode tracks relative to sections showing whisker barrels.

Physiological measurements. Data from intrinsic optical signal and electrophysiology experiments were acquired with a custom-written LabVIEW

program (ATW and PJD, version 11.0, 64-bit Windows 7, National Instruments, Austin, TX). Data from 2PLSM experiments was acquired using Sutter MCS software (Sutter Instruments, Novato, CA). All experiments were performed with mice in sound-attenuating boxes.

Habituation. Animals were gradually acclimated to head-fixation and stimulation over three habituation sessions. Mice that received whisker stimulation (n = 31) were acclimatized to head-fixation for 15–30 min during the first session. In subsequent sessions, they began to receive air puffs directed at the whiskers and were head-fixed for longer durations (> 60 minutes). Mice that were imaged using 2PLSM (n = 7) were acclimated to the spherical treadmill (60 mm radius) over 3 d. During the first session, they were head-fixed for 15 min. On subsequent days, the time was increased to ~60 min. Mice were monitored for any signs of distress during habituation. In all cases, the mice exhibited normal behaviors such as exploratory whisking and occasional grooming after being head-fixed. Heart-rate-related fluctuations were detectable in the intrinsic optical signal (see below) and remained between 7 and 14 Hz for all mice during and after habituation. This is near the mean heart-rate (~12 Hz) telemetrically recorded from mice in their home cage54. Experimental data were taken over 2–5 imaging sessions.

Intrinsic optical signal (IOS) imaging. Twenty minutes before the experiments, the mice were briefly (< 1 min) anesthetized with isoflurane (5%) and fixed into the imaging apparatus using the attached titanium head bar. When fixed, the animal’s body was supported in a clear plastic tube (n = 31) or on a ball (n = 4). The imaging apparatus and head-fixing apparatus were arranged to avoid incidental whisker contact during whisking. Blood volume was measured by illuminating the cranial window with three collimated and filtered 530 ± 5-nm LEDs (Thor Labs, FB530-10, M530L2). This wavelength is an isosbestic point of the hemoglobin light absorption curves. Reflected light intensity from the surface of the brain at this wavelength gives a measurement of the total hemoglobin concentration, which we used as a measure of CBV. Decreased reflectance from the cranial window corresponds to increased absorption by hemoglobin due to increased CBV. The cranial window was imaged with a Dalsa 1M60 Pantera CCD camera (Phase One, Cambridge MA) positioned directly above the mouse. Light entering the camera was filtered using a mounted green filter (Edmund Optics, Barrington NJ, #46540) to block the light used for whisker tracking. Images (256 × 256 pixels, 15 µ m per pixel, 12-bit resolution) were acquired at 30 Hz9,55. The data were then low-pass filtered (3 Hz, Butterworth, order = 4, Matlab function: butter, filtfilt) to remove heart-rate-related fluctuations.

Electrophysiology. Neural activity was recorded simultaneously with the IOS as differential potentials between the two leads of Teflon-insulated tungsten microwires (A-M Systems, #795500)5. Stereotrode microwires with an interelectrode spacing of ~100 µ m were threaded through polyimide tubing (A-M Systems, #822200). Electrode impedances were between 70 and 120 kΩ at 1 kHz. The acquired signals were amplified (World Precision Instruments, DAM80), bandpass filtered between 0.1 and 10 kHz (Brownlee Precision, Model 440) during acquisition, then digitized at 20 kHz (National Instruments, Austin TX, PCI-6259). We were able to detect individual spikes in all animals (Supplementary Fig. 1c), but spiking responses to whisker stimulation and volitional whisking varied among animals23. We quantified spiking activity using the multiunit average (MUA), which is the power of the signal within the 300–3,000 Hz band22. Power was calculated by digitally bandpass filtering the raw neural signal (Matlab function: butter, filtfilt; filter order = 4). The result was squared, low-pass filtered below 10 Hz, and resampled at 30 Hz. A baseline power (Pb) was computed by averaging the band-limited power during periods of the imaging session in which no stimulation or volitional movement occurred. The normalized power was then calculated as Δ P/Pb = [P− Pb]/Pb. Gamma-band power was calculated in the same manner using the 40–100 Hz band of the LFP. Neither the LFP nor the MUA signals showed significant changes in sensory evoked amplitude (gamma-band power: P = 0.94, t61 = 0.08; MUA: P = 0.19, t61 = 1.33) or neural variance (gamma-band power: P = 0.81, t61 = 2.45; MUA: P = 0.28, t61 = 1.09) from day to day (n = 63, iteratively reweighted least squares, bisquare weighting, Matlab function: robustfit), indicating that the recordings were stable across imaging sessions. The spatial resolution of the LFP depends on the distance between the recording electrodes. Differential recordings sample the volume of tissue spanned by the electrodes, and if these electrodes are closely spaced56,57, they provide a very localized (equivalent to the electrode spacing) measure of the LFP. Using a remote reference can contaminate the LFP signal if activity in the remote area is nonstationary, as is the case in an awake animal. Signals from spiking activity are much more localized, and our electrode recorded neural activity from a sphere of ~100 µ m in diameter58. For infusion experiments, band-limited neural power was normalized to the mean amplitude during periods of rest from the same session prior to infusion.

Two-photon microscopy. Mice (n = 7) were briefly anesthetized with isoflurane and retro-orbitally injected with 50 µ L 5% (weight/volume) fluorescein-conjugated dextran (70 kDa; Sigma-Aldrich)59,60, and then fixed on a spherical treadmill (see “Behavior tracking” section below). Imaging was done on a Sutter Movable Objective Microscope with a 20× , 0.9 NA objective (Olympus). A MaiTai

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ArticlesNature NeuroSCieNCeHP (Spectra-Physics, Santa Clara, CA) laser tuned to 800 nm was used for fluorophore excitation. Individual arteries (n = 19 pial, 5 vessels/mouse; n = 8 penetrating, 2–3 vessels/mouse) were imaged at nominal frame rate of 8 Hz for 5 min using 10–15 mW of power exiting the objective. The same arteriole segments in the limb representation of somatosensory cortex were imaged after aCSF and muscimol infusions.

Vibrissa stimulation. Animals were awake and engaged in whisking behavior during IOS data acquisition. Brief (0.1-s duration) puffs of air were delivered to the ipsilateral and contralateral whiskers7 through a thin plastic tube (length 130 mm, diameter 2 mm). Air puffs were directed to the distal ends of the whiskers at an angle parallel to the face to prevent stimulation of other parts of the head or face. An additional air puffer was set up to point away from the body for use as an auditory stimulus. The puffs were delivered via solenoid actuator valves (Sizto Tech Corporation, 2V025 1/4) at constant air pressure (10 psi) maintained by an upstream regulator (Wilkerson, R03-02-000). Air puffs were separated by intervals of 10–60 s, and the order of all sensory stimulation was randomized, with a nominal ratio of three contralateral stimuli for every ipsilateral or auditory stimulation. Auditory and ipsilateral stimuli were omitted from the principal analysis because their responses were primarily related to stimulus-provoked movement (Supplementary Fig. 1h).

Behavioral measurement. For IOS imaging experiments, the whiskers contralateral to the imaged hemisphere were diffusely illuminated from below with 625-nm light (Edmund Optics, #66-833) and imaged using a Basler A602f camera (Edmund Optics, Barrington NJ) at 150 frames per s. The imaging region was restricted to the area just adjacent to the face, with the long axis of the image parallel to the line of the face where whisker curvature is minimal and the full range of whisker movement could be captured. As additional behavioral measurements, the animal was monitored using a webcam (Microsoft LifeCam Cinema), and a force sensor (Tekscan, Flexiforce A201, Boston MA) was placed below the clear plastic tube to detect body movement. A threshold was established by examining force measurements when each animal was clearly moving during the trial. Changes in force that exceeded the threshold were flagged as body movements by the animal.

For 2PLSM imaging (n = 7) and for a subset of IOS experiments (n = 4), mice were fixed using the attached head bar on a spherical treadmill with one degree of freedom5,60. The treadmill was coated with antislip tape and attached to an optical rotary encoder (US Digital, E7PD-720-118) to monitor rotational velocity. For mice on the treadmill, ball movements were used to detect animal behavior in lieu of whisker tracking in order to identify periods of rest.

Intracortical infusions. For IOS imaging, animals were placed in the imaging apparatus as described above. We then acquired 10 min of CBV, neural, and behavioral data with the dummy cannula in place. The dummy cannula was then slowly removed and replaced with an infusion cannula (Plastics One, C315IS-4). The interface between the infusion cannula and the guide cannula was sealed with Kwik-Cast (World Precision Instruments). Muscimol (10 mM)61 alone, and a muscimol (10 mM) + CNQX (600 μ M) + AP5(2.5 mM) cocktail, a muscimol (10 mM) + prazosin (1 mM) + yohimbine (1 mM) + propranolol (1 mM) cocktail62, or aCSF alone were infused at a rate of 25 nL/min for a total volume of 500 nL. CBV and neural activity were constantly monitored during the infusion to ensure that cannula placement did not affect neural activity or hemodynamic signals. Autoradiography63, immunohistochemistry64, and gene expression65 studies have failed to detect GABA-A receptors in the small vessels of the cerebral vasculature whose dilations we measured here. Blocking GABA-A mediated transmission does not block optogenetically evoked vasodilation by interneurons66, indicating that GABA-A receptors do not mediate activity-evoked vasodilation. To validate that the cannula was patent, infusions were combined with 1% fluorescein isothiocyanate (FITC). To verify successful infusion, FITC fluorescence was visualized by capturing images of the thinned skull window while it was illuminated with blue light (470 nm, Thor Labs, M470L2) every ~5 min. Pharmacological treatments and aCSF were each infused in a counterbalanced order. All mice received both infusions of drugs and an aCSF control, so no randomization was needed. Neural and CBV measurements were acquired during the infusion as an additional means of assaying the efficacy of pharmacological manipulation. Pharmacological data were only used if the MUA amplitude was confirmed to have decreased by > 80% compared to aCSF. Baseline reflectance from the ROI increased as neural activity was increased, indicating decreased CBV. CBV baselines prior to this decrease were used for normalization. Some animals exhibited occasional bursts of neural activity accompanied by large CBV changes (10–15% peak to peak) approximately 1 h after muscimol infusions. Data taken after a burst of activity were omitted from subsequent analyses. For 2PLSM imaging, mice were head-fixed on the spherical treadmill. Infusion cannulas were placed and infusions were conducted as described for IOS imaging, except that FITC was not infused. Animals were kept in place for 20 min after the infusion and then moved to the imaging apparatus. The infusion cannulas were kept in place for the duration of the imaging session. Baseline diameter constrictions were observed during infusion, in accordance with intrinsic data. Vessel measurements occurred > 45 min after the start of infusion.

Data analysis. All analyses were conducted using custom-written code (by A.T.W., C.E., Q.Z., and P.J.D.) in Matlab 2015a (MathWorks).

Alignment of anatomical areas to CBV imaging data. The location of the thinned-skull window with respect to anatomical landmarks of the cortex was obtained by aligning the surface vasculature in the most superficial histological sections with the vasculature visible through the window during imaging. Deeper sections were aligned using fiduciary marks and penetrating vessels as landmarks. All image alignment was performed using affine transformations in Adobe Illustrator CS6 (Adobe Systems).

Detection and quantification of whisker motion. Images of whiskers during data acquisition were converted into a relative whisker position by applying the Radon transform (Matlab function: radon) to each image. Peaks from the resulting sinogram correspond to a position and angle of whiskers in the image. The mean whisker angle was extracted by identifying the angle of the sinogram that had the largest variance in the position dimension59. The mean whisker position was digitally low-pass filtered (< 30 Hz) using a fourth-order Butterworth filter (Matlab functions: butter, filtfilt). Whisker acceleration was obtained from the second derivative of the filtered position and binarized according the equation

δ = − =≥<

t H a aa aa a

( ) ( )1,0,t c

t c

t c

where at is the acceleration at time t, and ac is the acceleration threshold. Whisker acceleration was used since it is directly related to the force on the whisker. The whisking thresholds were empirically defined for each animal by identifying whisker accelerations that occurred when the animal was clearly whisking. Similarly, periods of rest were identified during which the animal was not making exploratory whisker movements, to establish acceleration values during nonwhisking periods. Any isolated acceleration values that fell between these thresholds were omitted from analysis. Ambiguous acceleration values that occurred within 0.5 s of a clear whisk were considered part of the bout of whisking.

2PLSM image processing. Individual frames from 2PLSM imaging were aligned using a rigid registration algorithm to remove motion artifacts in the x–y plane7,39. Visual inspection of movies indicated that there was minimal z-axis motion. A rectangular box was manually drawn around a short segment of the vessel and the pixel intensity was averaged along the long axis7. Pixel intensity was used to calculate diameter from the full-width at half-maximum. Diameters of penetrating arterioles were calculated using the thresholded in Radon space (TiRS) algorithm39,67. Periods of rest were segregated using locomotion events measured with the rotary encoder9. For each 5-min trial, diameter measurements were normalized to the average diameter during periods of rest. The diameters were smoothed with a third-order, 15-point Savitzky–Golay filter (Matlab function: sgolayfilt). During all resting periods, the spontaneous fluctuations were compared by calculating the normalized standard deviation from the average baseline diameter. The standard deviation of the fractional diameter changes after pharmacological infusion were normalized by the standard deviations after fractional diameter changes after aCSF infusion for the same vessel segment.

Heart rate detection. We tracked heart rates by calculating time–frequency spectrograms with a 2-s sliding window on the median window reflectance (Supplementary Fig. 2a; Chronux Toolbox version 2.11 function: mtspecgramc; http://chronux.org/)55,68. We took the derivative of the median window reflectance to reduce the 1/f trend in the spectrum before calculating the time–frequency spectrogram and to improve detection of peaks in spectral power. The heart rate was identified as the frequency with the maximum spectral power in the 5–15 Hz band.

Behavioral state categorization. For analysis of HRF dynamics, data were separated into three behavioral categories: contralateral whisker stimulation, volitional whisking, and rest. The period (6 s) following an air puff to the contralateral whiskers was isolated, provided that the animal was quiescent at the time of stimulation. Volitional whisking behaviors were defined as the period following a voluntary movement of the whiskers with at least 3 s of rest prior to the whisker movement. The duration of the movement was also tracked and separated into short (< 2 s) and long (> 2 s) movements. Resting behavior was defined as the absence of stimulation or movement. Periods of rest lasting < 10 s were considered to be transitions between behaviors and omitted from the principal analysis. Data corresponding to whisker stimulation and volitional whisking were isolated beginning 4 s before and 6 s a

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